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OSPF is like a traffic director for the internet. Imagine a city with many roads - OSPF helps routers (the traffic directors) figure out the best paths for data to travel from one place to another. It’s like a smart GPS for computers, making sure information takes the shortest and fastest routes. OSPF routers talk to each other, share maps of the internet, and decide the best ways to send data. It’s a cool system that keeps the internet running smoothly!

ChatGPT explaining OSPF to a high-school kid

Configuration Tips

This blog started as a collection of (hopefully) helpful configuration tricks, and I documented numerous Cisco IOS configuration tips in the early 2000s.

Implementation Details

Let’s start with the elephant in the room: OSPF areas – a simple concept that got way too convoluted when OSPF started accreting nerd knobs like NSSA areas:

OSPF default routes are another confusing topic. You could have inter-area default routes (used in stub areas) or external default routes that could be conditional or unconditional.

OSPF adjacencies are another fun troubleshooting topic:

The inimitable forwarding address in type-5 LSA will make your head explode when combined with the NSSA areas.

Want even more OSPF details? I documented way too many of them since I started blogging, including:

Deploying OSPF

Creative networking engineers often forget an unpleasant truth: OSPF is a single security domain. You should never run it with less-trusted peers, be it your customers, data center servers, or virtual machines.

OSPF by itself is complex enough, but the real fun starts when you combine it with other protocols (for example, BGP and LDP):

Running OSPF in large hub-and-spoke networks (for example, large DMVPN networks) is another tough challenge:

While you could use OSPF to get unequal-cost multipathing, you might be tripped by numerous caveats; no wonder there are few implementations of this concept.

Finally, you can run OSPF over unnumbered interfaces, be it point-to-point serial links or Ethernet segments:

Rants

Now and then, I couldn’t resist writing an OSPF-related rant:

What Others Are Writing About OSPF

automation

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MPLS VPN

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AI

Artificial Intelligence (AI) and Machine Learning (ML) are the next big hype in networking following Software-Defined Everything and Intent-Based Everything. Like with the previous hype bubbles it’s worth figuring out

How much of the hype is real (TL&DR: not much)?
Whether the technology is ready to be used in production networks (TL&DR: some of it)
How you could use the technology to make your life easier

How Real Is It?

Like with the previous hype tsunamis I’ll do my best to help you figure out the answers to the above questions with a hefty dose of skepticism and snark¹, starting with:

I also decided to “kick the tires” and document my (often less-than-stellar) experience with the most-overhyped products:

AI/ML in Networking: The Good, the Bad and the Ugly

Javier Antich created a wonderful AI/ML in Networking in 2021. If you know nothing about AI/ML and wonder whether you should care about it, you MUST watch these videos from his webinar:

In 2023, Javier published a book covering the same set of topics in way more details. I would highly recommend you read it if you want to know more.

What Others Are Saying

I keep collecting interesting articles talking about AI in general and (lately) ChatGPT. I found these interesting enough to mention them in worth reading blog posts:

Machine Learning Explained (2020)
AI Makes Animists of Us All (2022)
The AI Illusion (2022)
Collections: On ChatGPT (a Historian Perspective) (2023)
Putting Large Language Models in Context (2023)
The Dangers of Knowing Everything (2023)
Building Trustworthy AI (2023)
Cargo Cult AI (2023)
Building Stuff with Large Language Models Is Hard (2023)
Worth Reading: AI Does Not Help Programmers (2023)
Eyes that glaze over. Eyes like saucers. Eyes that narrow. (2023)
Networking for AI Workloads (2023)
Looking Inside Large Language Models (2023)
Where Are the Self-Driving Cars? (2023)
AI Risks (2023)
State-of-the-Art AI (2023)
Worth Reading: The AI Supply Paradox (2023)

These are not bad either:

Finally, a few real-life uses of large language models:

An Exploration of Embeddings and Vector Databases
How GPT and LLMs will affect documentation
I Built an AWS Well-Architected Chatbot with ChatGPT
Building Boba AI – how to build a custom user interface in front of a large language model.
Using Langchain to interact with ChatGPT

Blog Posts I Forgot to Categorize

PCAP Analysis with Generative AI

Please don’t blame me for pointing out the ever-lasting validity of Sturgeon’s law. Contrary to what some people think, I’m not trying hard to pick up dismal examples of AI failures, I’m just good at looking in the wrong places. Also, I’m too old to be wearing rosy glasses and drinking Kool-Aid. ↩︎

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